As a method of detecting a moving object such as a person that moves changing shape or performing segmentation on an image including the moving object, there is a combination of techniques of extracting a candidate region of an object from the image and of applying a previously-prepared object model to the extracted candidate region of the object (for example, see Patent Literature 1 and Non Patent Literature 1). For example, Patent Literature 1 discloses a method of extracting a silhouette image of the object such as a person from pictures as an object candidate region and then applying, to the extracted silhouette, a model which is related to the object such as the person and in which regions of the object are previously parameterized based on knowledge related to the object. With this, it is possible to apply the parameterized model to the moving object such as a person that moves changing shape, thus allowing detection of the moving object and performance of segmentation.
In addition, Non Patent Literature 1 discloses a method of allowing images captured from similar viewpoints to be projected close-range on a two-dimensional space by: calculating a Euclidean distance between pixel value data in each image and pixel value data in the other images, using an input of images of a fixed moving object captured from plural viewpoints, and then performing geodetic distance transformation on the Euclidean distance, and then performing dimensional compression thereon. The literature shows that compared to a conventional linear dimensional compression method such as Principal Component Analysis (PCA), it is possible to perform lower-dimensional compression through geodetic distance transformation, and further to process the data that is non-linearly distributed.